class_docstr: A replay buffer shard for multiagent setups.
  
  This buffer is meant to be run in parallel to distribute experiences
  across `num_shards` shards. Unlike simpler buffers, it holds a set of
  buffers - one for each policy ID.
docstr: Returns all local state.
  
  Returns:
      The serializable local state.
docstr_tok: ["Returns","all","local","state",".","Returns",":","The","serializable","local","state","."]
code: def get_state(self) -> Dict[str, Any]:
      """Returns all local state.
  
      Returns:
          The serializable local state.
      """
      state = {"num_added": self._num_added, "replay_buffers": {}}
      for policy_id, replay_buffer in self.replay_buffers.items():
          state["replay_buffers"][policy_id] = replay_buffer.get_state()
      return state
  
imports: import collections
  import logging
  from enum import Enum
  from typing import Any, Dict, Optional
  from ray.util.timer import _Timer
  from ray.rllib.policy.rnn_sequencing import timeslice_along_seq_lens_with_overlap
  from ray.rllib.policy.sample_batch import MultiAgentBatch, SampleBatch
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.deprecation import Deprecated
  from ray.rllib.utils.from_config import from_config
  from ray.rllib.utils.replay_buffers.replay_buffer import _ALL_POLICIES, ReplayBuffer, StorageUnit
  from ray.rllib.utils.typing import PolicyID, SampleBatchType
  from ray.util.annotations import DeveloperAPI
  from ray.util.debug import log_once
label_desc: Use image node_id 8 for calling the MultiAgentReplayBuffer obj's underlying member method code with example usage: obj.get_state() and returns: state